Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Probabilistic Visual Learning for Object Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Eigenfaces vs. Fisherfaces: Recognition Using Class Specific Linear Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
The FERET Evaluation Methodology for Face-Recognition Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Beyond Eigenfaces: Probabilistic Matching for Face Recognition
FG '98 Proceedings of the 3rd. International Conference on Face & Gesture Recognition
Solving the Small Sample Size Problem of LDA
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
Face recognition: A literature survey
ACM Computing Surveys (CSUR)
A Unified Framework for Subspace Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminative Common Vectors for Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Rapid and brief communication: Why direct LDA is not equivalent to LDA
Pattern Recognition
Eigenfeature Regularization and Extraction in Face Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complete discriminant evaluation and feature extraction in kernel space for face recognition
Machine Vision and Applications
Asymmetric Principal Component and Discriminant Analyses for Pattern Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dual-space linear discriminant analysis for face recognition
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Convergent 2-D Subspace Learning With Null Space Analysis
IEEE Transactions on Circuits and Systems for Video Technology
Ensemble-based discriminant learning with boosting for face recognition
IEEE Transactions on Neural Networks
A two-stage linear discriminant analysis for face-recognition
Pattern Recognition Letters
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We present a prediction and regularization strategy for alleviating the conventional problems of LDA and its variants. A procedure is proposed for predicting eigenvalues using few reliable eigenvalues from the range space. Entire eigenspectrum is divided using two control points, however, the effective low-dimensional discriminative vectors are extracted from the whole eigenspace. The estimated eigenvalues are used for regularization of eigenfeatures in the eigenspace. These prediction and regularization enable to perform discriminant evaluation in the full eigenspace. The proposed method is evaluated and compared with eight popular subspace based methods for face verification task. Experimental results on popular face databases show that our method consistently outperforms others.